Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation GPT-4 Technical Report

Reference 1

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Unresolved cited work

Reference 2

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This paper cites Flamingo: a visual language model for few-shot learning,.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Flamingo: a visual language model for few-shot learning,

Reference 3

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This paper cites Neural machine translation by jointly learning to align and translate, 2016.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Neural machine translation by jointly learning to align and translate, 2016

Reference 4

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This paper cites Neural sign language trans- lation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Neural sign language trans- lation

Reference 5

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This paper cites Sign language transformers: Joint end-to- end sign language recognition and translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Sign language transformers: Joint end-to- end sign language recognition and translation

Reference 6

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This paper cites Vlp: A survey on vision-language pre-training.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Vlp: A survey on vision-language pre-training

Reference 7

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This paper cites A simple multi-modality transfer learning baseline for sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation A simple multi-modality transfer learning baseline for sign language translation

Reference 8

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Observation c55e7028-f33c-445e-8d3d-ccd135d3033b · outbound

This paper cites Two-stream network for sign language recognition and translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Two-stream network for sign language recognition and translation

Reference 9

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This paper cites Uniter: Universal image-text representation learning.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Uniter: Universal image-text representation learning

Reference 10

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This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, 2024

Reference 11

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Observation af90ec1d-5d54-492f-9bb1-e65b0f2cf57c · outbound

This paper cites Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation

Reference 12

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This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 13

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This paper cites Imagenet: A large-scale hierarchical image database.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Imagenet: A large-scale hierarchical image database

Reference 14

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This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding, 2019.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Bert: Pre-training of deep bidirectional trans- formers for language understanding, 2019

Reference 15

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This paper cites Cross-modal neural sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Cross-modal neural sign language translation

Reference 16

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This paper cites A token-level contrastive framework for sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation A token-level contrastive framework for sign language translation

Reference 17

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Observation 487b59a8-5b83-4032-835d-3d2a70406dab · outbound

This paper cites Llms are good sign language translators.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Llms are good sign language translators

Reference 18

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This paper cites MultiModal-GPT: A Vision and Language Model for Dialogue with Humans.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation MultiModal-GPT: A Vision and Language Model for Dialogue with Humans

Reference 19

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This paper cites Deep residual learning for image recognition, 2015.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Deep residual learning for image recognition, 2015

Reference 20

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This paper cites Egolm: Multi-modal language model of egocentric motions, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Egolm: Multi-modal language model of egocentric motions, 2024

Reference 21

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This paper cites LoRA: Low-rank adaptation of large language models.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation LoRA: Low-rank adaptation of large language models

Reference 22

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Unresolved cited work

Reference 23

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This paper cites An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs

Reference 24

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This paper cites Unsupervised dense information retrieval with con- trastive learning, 2022.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Unsupervised dense information retrieval with con- trastive learning, 2022

Reference 25

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 26

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This paper cites Visual alignment pre-training for sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Visual alignment pre-training for sign language translation

Reference 27

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This paper cites Prior knowledge and memory enriched transformer for sign lan- guage translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Prior knowledge and memory enriched transformer for sign lan- guage translation

Reference 28

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This paper cites Llava-onevision: Easy visual task transfer,.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Llava-onevision: Easy visual task transfer,

Reference 29

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This paper cites Tspnet: Hier- archical feature learning via temporal semantic pyramid for sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Tspnet: Hier- archical feature learning via temporal semantic pyramid for sign language translation

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This paper cites Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models, 2024

Reference 31

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This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 32

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023

Reference 33

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This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

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This paper cites Rouge: A package for automatic evaluation of summaries.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Rouge: A package for automatic evaluation of summaries

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This paper cites Visual instruction tuning.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Visual instruction tuning

Reference 36

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This paper cites UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Unresolved cited work

Reference 38

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Min and X

Reference 39

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This paper cites Mochat: Joints-grouped spatio-temporal grounding llm for multi-turn motion com- prehension and description, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Mochat: Joints-grouped spatio-temporal grounding llm for multi-turn motion com- prehension and description, 2024

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This paper cites Bleu: a method for automatic evaluation of machine translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Bleu: a method for automatic evaluation of machine translation

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This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Kosmos-2: Grounding Multimodal Large Language Models to the World

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This paper cites Learning transferable visual models from natural language supervi- sion.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Learning transferable visual models from natural language supervi- sion

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This paper cites All you need in sign language production, 2022.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation All you need in sign language production, 2022

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This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Sentence-bert: Sentence embeddings using siamese bert-networks

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This paper cites Sign lan- guage gesture recognition.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Sign lan- guage gesture recognition

Reference 46

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This paper cites Stokoe, William C.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Stokoe, William C

Reference 47

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This paper cites Mul- tilingual translation with extensible multilingual pretraining and finetuning, 2020.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Mul- tilingual translation with extensible multilingual pretraining and finetuning, 2020

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This paper cites Alpaca: A strong, replicable instruction- following model.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Alpaca: A strong, replicable instruction- following model

Reference 49

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation LLaMA: Open and Efficient Foundation Language Models

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This paper cites Attention is all you need.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Attention is all you need

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This paper cites The chal- lenges of cross-modal translation: English-to-sign-language translation in the zardoz system.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation The chal- lenges of cross-modal translation: English-to-sign-language translation in the zardoz system

Reference 52

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This paper cites Stochastic transformer networks with linear competing units: Application to end-to-end sl translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Stochastic transformer networks with linear competing units: Application to end-to-end sl translation

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Observation 27a2d85b-24f4-4dfb-93f9-dc476566e13f · outbound

This paper cites Text embeddings by weakly-supervised contrastive pre- training, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Text embeddings by weakly-supervised contrastive pre- training, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.108602Z

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Observation 196f4d4e-6c0b-40f6-a1dc-e9989fe03004 · outbound

This paper cites Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.097641Z

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Observation fa6ca9c6-ae4c-4d06-b0e6-3e7c3c229a8d · outbound

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

Reference 56

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Source-reported events for the cited work

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Observation be9ff8d1-144b-420e-8266-be58b41a4f6a · outbound

This paper cites Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.770507Z

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Observation bf34ee9b-7752-4bb7-81d5-bd1eedefdacd · outbound

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Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:29:39.086143Z

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Observation 06388f9e-7130-4f78-9e7f-2b2f371992f7 · outbound

This paper cites Qwen2 technical report, 2024.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Qwen2 technical report, 2024

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.074837Z

Source-reported events for the cited work

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Observation 60393313-46e1-47ce-ab04-12388d1ba672 · outbound

This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.781663Z

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Observation 6889a2a1-e5ac-472f-9036-c9bf778963c6 · outbound

This paper cites Simulslt: End-to- end simultaneous sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Simulslt: End-to- end simultaneous sign language translation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.063297Z

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Observation 740c7d74-4cc6-4822-a170-3af1a23e7163 · outbound

This paper cites Gloss attention for gloss-free sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Gloss attention for gloss-free sign language translation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.788960Z

Source-reported events for the cited work

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Observation 48148b8c-9e70-4f67-80ef-e8349592c4dd · outbound

This paper cites SLTUNET: A Simple Unified Model for Sign Language Translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation SLTUNET: A Simple Unified Model for Sign Language Translation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.792419Z

Source-reported events for the cited work

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Observation 0d17e710-c343-4ceb-9801-61fd116c416a · outbound

This paper cites SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.796201Z

Source-reported events for the cited work

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Observation 41be5ed8-ee64-4fe3-9e96-edd6fdaf443b · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.799820Z

Source-reported events for the cited work

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Observation 7ca34b3c-7f03-4ce3-9577-2ad217947891 · outbound

This paper cites Conditional sentence generation and cross-modal reranking for sign language translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Conditional sentence generation and cross-modal reranking for sign language translation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.045769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 00dc8db9-e615-46f8-9a16-b49d0b018fcd · outbound

This paper cites Gloss-free sign language translation: Improving from visual- language pretraining.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Gloss-free sign language translation: Improving from visual- language pretraining

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.033916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1af92b6-9f01-4266-8d45-f777d5fddd10 · outbound

This paper cites Improving sign language translation with monolingual data by sign back-translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Improving sign language translation with monolingual data by sign back-translation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.021941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d2ad3ef-9cab-4938-ab2c-b25cf8ebfe64 · outbound

This paper cites Spatial-temporal multi-cue network for sign language recog- nition and translation.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation Spatial-temporal multi-cue network for sign language recog- nition and translation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:39.010503Z

Source-reported events for the cited work

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Observation 405515db-d559-45d8-9f7b-d2a23873df5b · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:38.817694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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